Google's NotebookLM is a great product that will die on the vine
Google doesn't have the entrepreneurial mettle to build a business out of NotebookLM
Startup opportunities with a NotebookLM-like product
Google’s NotebookLM shows promise as a next-gen productivity tool, combining AI and document parsing and analysis in a way that could revolutionize knowledge management1. But like many of Google’s innovations, it risks falling victim to the company’s notorious habit of abandoning high-potential projects. This creates a unique opportunity for startups to step in and build something more focused, nimble, and sustainable.
However, any startup attempting to replicate or improve upon NotebookLM would find itself in a challenging position: as an intermediary between foundational model providers (Google, OpenAI, etc.) and enterprise customers. Relying heavily on these providers puts startups at risk, as they are beholden to the changing priorities, pricing, and policies of the giants. Nevertheless, there are strategic ways to navigate this intermediary trap and thrive in the AI productivity space2.
Specialization and vertical focus
Rather than trying to build a general-purpose tool like NotebookLM, startups should focus on hyper-specific workflows within targeted verticals. By going deep into niches like legal research, scientific discovery, or financial analysis, startups can offer significant, specialized value that a broad tool like Google’s cannot match.
For example, a legal research tool could be optimized for automated case law citations, or a scientific discovery tool might offer in-depth chemical compound analysis and experiment tracking. By delivering tailored features and expert-level refinement in a specific industry, a startup can create defensible territory. The deeper the specialization, the harder it is for Google or OpenAI to compete with their more generalized tools.
Build a proprietary layer on top of foundational models
Foundational models like GPT-4 or PaLM are becoming commodities. The real opportunity for startups lies in building proprietary layers on top of these models that offer features or integrations that foundational model makers won’t easily replicate. This might involve fine-tuning models with highly specialzized datasets, building domain-specific automations, or integrating proprietary tools.
For instance, a startup focused on financial analysis could train its models with decades of SEC filings, earnings call transcripts, and financial metrics. This would create an AI tool that offers insights far beyond the capabilities of a generic model. The more proprietary the layer, the more valuable the tool becomes—and the less reliant the startup is on any single foundational model provider.
Enterprise-level customization and support
Enterprises don’t just need powerful models—they need models that fit seamlessly into their infrastructure and meet their regulatory and compliance needs. Foundational model makers like Google are too focused on the mass market to provide the deep customization that enterprises require.
Startups can differentiate themselves by focusing on enterprise-level customization, integration and support. This includes offering highly customizable user interfaces, integrating with existing enterprise systems (CRM, ERP), and ensuring compliance with regulations such as HIPAA or GDPR. Additionally, startups can win customer loyalty by offering hands-on technical support and consulting services that Google or OpenAI can’t deliver at scale.
Prioritize data sovereignthy and security
One of the biggest concerns enterprises have with AI tools is the security of their data. A startup that prioritizes data sovereignty and security can win trust where foundational model providers fall short. This could mean offering on-premise deployments, where sensitive data never leaves the company’s infrastructure, or leveraging federated learning to ensure that models are trained without exposing proprietary data.
This approach is particularly appealing to industries like healthcare and finance, where strict regulations around data privacy are non-negotiable. By providing a level of security and control that larger AI providers don’t offer, startups can position themselves as the go-to solution for enterprise clients that need full control over their data.
Create user-friendly interfaces and integrations
While foundational model providers focus on building the best AI models, startups have an opportunity to win on usability. A key competitive advantage for a startup could be building a notebook interface that integrates seamlessly with a company’s existing tools—whether it’s CRM, ERP, or other collaboration software.
A notebook that can sync with Salesforce, automate reporting in Excel, or pull in data from enterprise databases in real-time would save organizations countless hours of manual work. Startups can win by providing the flexibility and tailored integrations that larger companies like Google can’t.
Build for flexibility and resilience in a shifting ecosystem
A critical risk for any AI startup is dependence on foundational model providers, which could change terms or withdraw support at any time. To mitigate this risk, startups should architect their systems to be model-agnostic. This means building multi-model capabilities, allowing the startup to swap out one foundational model for another based on performance, pricing, or availability.
For example, a startup could allow its users to choose between models from Google, OpenAI, or Anthropic, depending on the task at hand. This flexibility not only shields the startup from the whims of any single provider but also allows them to offer clients the best possible model for each use case.
Conclusion: Outmaneuver the giants
While the potential for a NotebookLM-like product is substantial, the path to success for a startup lies in specialization, customization, and enterprise-level trust. By focusing on specific verticals, building proprietary layers on top of foundational models, and offering flexibility send security that the giants can’t, startups can carve out a unique place in the market. The opportunity is there for those who can execute with precision and agility—and outmaneuver Google by offering what they can’t: focus, depth, and adaptability.
I’ll share an example of a notebook I created, here, but it’s not entirely clear to me whether this sharing function actually works. As powerful as NotebookLM is, it is also in many respects a half-baked product.
Another risk, outside the scope of this post, is that foundational models become so powerful that they obviate much of, if not all of, the feature set that startups build on top of them.